Biometric matching or authentication systems are used for enrolling and authenticating users of devices incorporating the authentication systems. Biometric sensing technology provides a reliable, non-intrusive way to enroll and verify individual identity for authentication purposes.
A common biometric feature used for authentication is a fingerprint. Fingerprints, like certain other biometric characteristics, are based on unalterable personal characteristics and thus are a reliable mechanism to recognize individuals. There are many potential applications for utilization of biometric and fingerprints sensors. For example, electronic fingerprint sensors may be used to provide access control in stationary applications, such as security checkpoints. Electronic fingerprint sensors may also be used to provide access control in portable applications, such as portable computers, personal data assistants (PDAs), cell phones, gaming devices, navigation devices, information appliances, data storage devices, and the like. Accordingly, some applications, in particular portable applications, may require electronic fingerprint sensing systems that are compact, highly reliable, and inexpensive.
In biometric matching and authentication systems, a primary goal is to determine whether a verification view of the biometric feature such as a fingerprint is a match or not with an enrollment template that stores multiple enrollment views of the biometric feature. In general, there are two types of errors associated with biometric recognition: false acceptance and false rejection. False acceptance occurs when there are enough similarities between fingerprints of two different individuals, that one may be mistaken for the other. For example, false acceptance may occur when the verification view of an imposter (a user not associated with the enrollment views) is sufficiently similar to the enrollment view(s) in the enrollment template (associated with a user registered with the system). False acceptance is often quantified by a false acceptance rate (“FAR”). False rejection occurs when the user registered with the system is not identified as the registered user. For example, false rejection may occur when a (registered) user provides an input fingerprint view which is not accepted as matching enrollment views previously provided by the same user. False rejection is often quantified by a false rejection rate (“FRR”).
Fingerprint sensors can develop fixed-location artifacts over time, for example, due to physical scratches or partially damaged electronics (e.g., dead pixels, dead columns, etc) which may affect the FAR and the FRR. It is therefore desirable to identify and manage fixed-position artifacts in biometric sensors.